Apache Flink: Stream Processing for All Real-Time Use Cases

This post details how Apache Flink supports three key categories of use cases: event-driven applications, real-time analytics, and streaming data pipelines. For event-driven applications, it highlights fraud detection, anomaly detection, alerting, real-time routing, business process monitoring, and IoT applications, emphasizing Flink's customizable window logic, per-event stateful processing, and Complex Event Processing (CEP) library with MATCH_RECOGNIZE. For real-time analytics, it covers ad/campaign performance, content performance, telco network quality monitoring, and pattern analysis, showcasing Flink's low-latency processing, Flink ML library, time-series analysis capabilities via CEP, and materialized views. For streaming data pipelines, it discusses ETL/ELT, data transformation, and data enrichment, emphasizing Flink's ability to handle large-scale data movement and transformation. The post also links to related resources on Flink SQL and Confluent Cloud for Flink.